We talk to a lot of T&S people who land on our site, poke around, and aren't quite sure what fits their situation. That's fair; we offer a few different tools that serve different needs, and "AI-powered Trust & Safety tooling" doesn't exactly narrow it down.

So here's a plain-language breakdown of what we do, how our products differ, and how to figure out which one (or which combination) actually makes sense for you.

The short version of what we do

Musubi builds AI infrastructure for Trust & Safety teams. We have two core products:

PolicyAI moderates content. You write your policies in plain language, and PolicyAI enforces them consistently, at scale, and with a reason given for every decision.

AiMod identifies bad actors at the account level. It learns from your moderators' decisions (and from behavioral and metadata signals) to detect spam, scams, bots, and fraud, and gets better over time.

The two products work independently or together. Together is usually more powerful.

PolicyAI: for teams who need consistent, explainable enforcement

If your main challenge is content (messages, posts, images, listings, usernames, videos) and you need to enforce written policies at scale, PolicyAI is probably where to start.

The core idea: instead of training a custom classifier or wrestling with a vendor's pre-built labels, you write your policies the way you already write them. PolicyAI reads them and gives you the information you need to enforce your policies. Update your policy, and enforcement updates immediately. No engineering ticket, no retraining.

Every decision comes back with a label, a severity score, a confidence score, and a rationale. That rationale matters more than it sounds because it's what lets your moderators understand and calibrate the system, and it's what you show a user when they appeal.

A few things worth knowing:

It supports text, images, audio, video, and multi-modal combinations. That last one is underrated— evaluating an image and its caption together often catches things that evaluating them separately would miss. Text moderation is in 100+ languages (everything frontier LLMs cover).

You can choose your model tier. Faster and cheaper for high-volume, lower-stakes content. A reasoning model for complex or borderline cases where accuracy matters more than latency. Most teams use a mix, and some escalate from one to another.

You make enforcement decisions. We connect via a single API: you send us content, we send back the outputs (label, rationale, severity, confidence). Then you decide what to do with those outputs. For example, you can automatically enforce on clearly violative and safe content, but esclate the content with mid-range confidence scores to your human moderation team.

It includes tooling to help you write better policies. If you've ever tried to translate a human-readable community guidelines doc into something an LLM will enforce predictably, you know how finicky that process is. We've built a policy optimizer to help with that, and a feature called Content Atlas that clusters similar content so you can spot patterns and find gaps before they become incidents. (More on what Content Atlas can do here, plus a little playground to test it out)

PolicyAI is a good fit if you're dealing with policy complexity. For example if your content rules have nuance that simple classifiers can't handle, or if you need to explain your decisions to users, regulators, or your own team.

AiMod: for teams who need to get ahead of bad actors

If your main challenge is fraud, spam, scam accounts, bots, or fake profiles, and you feel like you're always one step behind, AiMod approaches the problem differently.

Instead of enforcing written rules, AiMod learns. It watches what your moderation team does, combines that with behavioral signals (how fast is this user messaging? how long after signup did they start?) and metadata patterns (email characteristics, IP patterns, device signals), and builds a model of what bad looks like in your specific community.

Because it's learning from your data rather than generic training sets, it adapts to the particular tactics people use to abuse your platform, which tend to be pretty specific to your vertical and user base.

You can use it proactively by scoring accounts at signup, at the first message, at key behavioral triggers. Or reactively, after a user report comes in. Most teams do both.

One thing worth flagging: AiMod needs moderator training data to work well. If you're a very early-stage platform without much moderation history yet, we can talk through what that looks like for your situation.

Using both together

PolicyAI and AiMod can communicate with each other, which lets you build a complete moderation system that neither tool could handle alone.

The most common pattern:  PolicyAI is removing a user's content over and over, but the account itself hasn't crossed any threshold. That pattern is a signal worth escalating, so the account gets sent to AiMod for a fuller review, and if AiMod agrees, you take an account-level action instead of playing whack-a-mole with individual posts.

The reverse also happens: an account looks borderline suspicious in AiMod, but not clearly enough to act on. So instead of making a call on behavioral signals alone, the system pulls PolicyAI's content labels for that account. For example, has this user been posting things that also look problematic? The combined picture is usually much clearer.

If you're dealing with adversarial users who know how to stay just below the line on any single signal, running both in a loop closes a lot of those gaps.

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Integrations

We offer Musubi Coop, a hosted version of open-source moderation platform Coop, for teams that need moderator dashboards, queue management, and orchestration on top of the AI. Our core focus is the AI tools, but Coop is there if you need it.

We also integrate with Tremau's Nima platform, which is another great choice.

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"Which one do I actually need?"

Here's a rough heuristic:

Start with PolicyAI if: your primary pain is inconsistent enforcement, you need explainable decisions, you're in a regulated environment, or your policies are complex and nuanced.

Start with AiMod if: your primary pain is spam, scams, bots, or fake accounts, especially if the tactics keep evolving and rule-based approaches aren't keeping up.

Use both if: you have meaningful content policy complexity AND a significant bad-actor problem. Most mature platforms eventually end up here.

Add COOP if: you don't have existing moderation infrastructure and need a full system with moderator queues and decision orchestration.

A note on what we're not

We're not a pre-built classifier you buy off the shelf. If you want to point an API at your content and have it return labels based on someone else's idea of what's acceptable, there are straightforward options for that.

What we do is give your team real control over the policies, logic, and what the AI learns from. That requires some upfront work and configuration. In return, you get a system that actually reflects your platform's values and adapts to your specific threat environment, rather than a generic model you're constantly fighting.

What to do if you want to learn more

If any of this sounds like it fits what you're working on, or if you're not sure and want to talk it through, hit the "book a demo" button below. We're generally happy to have a no-pitch conversation about what you're dealing with and whether we're actually the right fit.